Decision making and implementation system

ABSTRACT

A system and method for generating recommendations of analyses of circumstances in business, accounting, science, medicine and other fields. An algorithm is generated using an interactive generation process based on decision tree type inquiries. The algorithm is translated into a computer language and code and loaded onto a computer, preferably on a network. A user inputs data concerning a particular topic, and the algorithm processes the data to generate and display a set of recommendations or analyses. The user inputs additional data which the system uses to refine the initial recommendations or analyses, and this process is repeated until arriving at a final set of recommendations or analyses. The organization and content of sets of display screens changes dynamically as data is input and processed. The data may include degrees of certainty relating to certain data, which is used in both determining a set of recommendations or analyses and expressing a degree of certainty about such recommendations or analyses.

FIELD OF THE INVENTION

This invention relates to the field of logic systems and devices forassisting in the making of decisions or otherwise weighing alternativesin complex factual scenarios. The system and devices also are useful inpreparing and implementing plans for complex analysis. The invention hasapplicability in, among other areas, analysis and decision-making inbusiness settings such as due diligence or structuring conducted inconnection with a business transaction, or considering and implementingemployee-retention systems. The invention may also be used in any othersetting requiring logical analysis of complex systems, such as scienceand medicine.

BACKGROUND OF THE INVENTION

Decision-making and analysis in business, as well as in other fields,has traditionally been conducted in an ad-hoc manner. In the businesssetting, a decision-maker such as an executive in finance or businessdevelopment or some other manager gathered facts he deemed material tothe decision and employed in-house advisors or referred to outsideexpert consultants. Alternatives were discussed and weighed, anddecisions were made.

Such an approach can be flawed in several ways. Because the approach isnot systemized, the decision may be made without a complete and accuratecollection of important factual data. In other words, there may not be a“checklist” for ensuring that all of the desired facts are input intothe decision-making process. As a result, facts that should be weighedare not, and it is even possible that facts that should not be weighedare. For example, outside expert consultants especially will makeassumptions about the goals that the decision-making process seeks toachieve or even about the applicable factual setting. These assumptions,based largely on the outside expert consultant's experience, may not beaccurate in all cases. Moreover, the outside expert consultant may be soingrained with his past experience that he may make these assumptionswithout even being aware of doing so.

The quality of both in-house advisors and outside expert consultantsvaries widely. Even the established professional service firms such aslarge accounting firms, law firms and management consultant firms thathave well-deserved reputations for quality and creativity, occasionallyrely too heavily on relatively junior and inexperienced people. Thelesser known firms include highly-competent professionals along withless-competent individuals. In-house advisors tend to know the companybusiness quite well in comparison to outside expert consultants, and aretherefore less prone to factual mistakes, but often are not steeped inthe area that is the subject of an important decision; for example, anin-house advisor usually does not analyze sizeable mergers of hiscompany on a day-to-day basis.

The decision-making process and analysis that is traditionally employeddoes not document the process well. Because the systems tend to beexperiential and intuitive, there is little record of why a decision wasmade or the facts upon which it was based. This is particularlyproblematic if the decision is later challenged by shareholders orothers.

In addition to all the flaws, outside expert consultants are usuallyquite expensive. It has been estimated that in the United States alonebusinesses spend over $200 billion annually on consultants, lawyers,accountants and other specialty advisors. As noted above, some of thismoney may be spent for services that are often misdirected, often notdelivered on a sufficiently timely basis, occasionally incompetent, andusually poorly documented.

There have been attempts at developing more systematic and documentedroutines for decision-making using modem computer techniques anddatabases. So-called “business intelligence software providers” such asHyperion Solutions, Microstrategy and Cognos offer software designed tofront-end with existing user databases and ERP systems. This softwaremines information residing elsewhere in the user's organization toprovide managers with real-time operational statistics such as amount ininventory and daily sales figures. Such software, however, is notespecially designed for the decision-making process, but rather forquick access to rapidly changing information about the user'sorganization.

Another source of information and decision support services ismanagement education providers and the executive-training centers atmany of the college and university business schools. These organizationsissue reports, present conferences and provide training to disseminatemanagement “best practices.” This is a highly fragmented industry with alargely ad hoc approach which seldom offers a saleable and scaleableproduct for systemized decision-making.

Business information providers such as Bloomberg, Gartner Group,Forester and Hoovers provide both synthesized and unsynthesized businessinformation. The expertise of these groups is usually specific toparticular industries (such as technology in the cases of Gartner andForester) or specific to particular kinds of information (such as debtand credit ratings in the case of Standard & Poors. These organizationshave a high degree of expertise in their developed fields ofinformation, but do not offer wide ranging products that systemize thedecision-making process for general application.

It can be appreciated that there is a need for a system that takesadvantage of the sizeable databases available in, and the highprocessing capability of, modern numeric processing equipment to deliverdecision support and implementation. Such a system should ideallyproduce sound recommendations based on considered analysis utilizingflexible databases containing extensive information. In a preferredembodiment, such a system would be interactive with and tailored to thespecific needs and circumstances of each user, and would document forlater retrieval the analysis made, the outcome, and the input factsrelied upon.

In addition, such a system would ideally take advantage of large privateand public networks such as the Internet. The use of the Internet wouldallow near-universal access and a concurrent presentation platform anduser interface, and also allow the system to draw on the vastinformation content available on world-wide Internet servers. Finally,such a system ideally would take advantage of the expertise and goodwillheld by existing professional service and consulting firms.

SUMMARY OF THE INVENTION

The present invention addresses these and other shortcomings in theprior art. In a preferred embodiment, the invention is presented as aset of Internet-based decision-support tools in an application serviceprovider (“ASP”) format, but may also be presented in other formats thatare not ASP-related or do not utilize the Internet, such as bydiskettes, CD ROMs or programs downloaded via a public or privatenetwork, all as discussed in greater detail below.

The system can be used in a variety of decision-support and analysisapplications, including, without limitation, business applications suchas mergers and acquisitions, annual strategic planning, employeerelations, due diligence, globalization strategy and implementation,risk management, and compensation. The system is also applicable inanalyzing other complex systems, including in science, medicine,engineering.

The invention has several aspects. One aspect involves the methodologyof providing expert decision-making support through an easily-accessiblenetwork such as the Internet utilizing software that is optimized forthe particular field that is the subject of the decision or analysis.The tools are presented in a hierarchy of modules, submodules and pagescontaining information and requesting data from the user in the mannerdescribed below. The owner or licensee of the system realizes revenue onthe user's use of the system through a scheme of user subscription feesor per-use fees or a blended fee approach.

Expert assistance in designing the system, and market recognition, isachieved, in part, by cooperative arrangements with well-knownprofessional service or consulting firms. These firms benefit from theexposure they receive in being associated with the product and fromclient referrals via the product. They may also receive fees for theirservices in the form of traditional hourly fees, fees based on theextent of use of the particular product with which their name isassociated, fees per unit of time (such as fixed monthly fees) or anycombination of the foregoing. In an Internet implementation, the websitepresenting the system can be hyperlinked to other websites includingthose of the professional service and consulting firms involved indeveloping the system. The system also tracks the use of the materialsdeveloped by the professional service and consulting firms and providessuch tracking information to those firms for their marketing andbusiness use.

One function of the system is to frame complex tasks and issues, and toorganize and implement decisions faster and more effectively usingdecision modules. At the same time, the system archives electronicallythe flow of decision-making for future reference. A second function isto allow product managers operating in the organization that owns orleases the system to interact with outside experts efficiently and tocreate new decision-making modules and to update existing modules.

The kinds of decision-support modules in the system include generalmodules with applicability for a wide range of decision support;industry-specific modules for ascertaining data specific to particularindustries and with input inquiries directed toward those industries;products that are created especially for a particular company ordivision of a company; and modules that are customized for severalcompanies or groups of companies. These kinds of modules, and theseveral modules within each kind, share certain logical frameworks orarchitectures summarized below and described in greater detail under theheading, “Detailed Description of a Preferred Embodiment.”

The base unit in the logical framework is a module itself. A module is aset of interrelated content with a defined purpose. Each module may be a“submodule” in relation to a module that is in a higher level of thehierarchy; and each module may have submodules in relation to it. Acharacteristic of modules, whether or not they have submodule status inrelation to other modules or have other modules that are submodules inrelation them, is that they output a discrete decision or analysis withrespect to their subject matter. In addition, they have associatedinformation such as bywords, date of creation or update, and a groupingof page hierarchy. A module may also have or include branding or URLs ofcompanies or firms that contributed to its creation or maintenance, andbiographical information for experts.

A page is the basic individual unit of presentation of informationwithin a module. Pages are grouped into sections or one or more pagesand each section has a priority. Like modules, sections may containseveral other sections which are subsections in relation to the sectionsin which they are contained. Each section is assigned a priority number.When the system navigates into a section, the priority number of thesections at the same level are noted, and the section with the highestpriority level is navigated to next.

Each page includes a number of attributes. One is the page preconditionwhich specifies a formula or other terms and the conditions under whicha client is allowed to access the page. These conditions may be, forexample, suitable answers to specific queries posed to the client whichestablish the page's applicability. The outcome of a condition formulamust be binary, i.e., yes or no, so that the page is either presented ornot presented.

The page priority is a number applicable to the data elements on thepage. This priority applied to the data elements is used in the maximumimpact procedure and the calculated importance procedure describedbelow, but does not affect client navigation. In a preferred embodiment,numeric priority ranges from 1 to 100, but any range scheme may be used.

The page is typically broken into a useable number of separateparagraphs. Each paragraph is identifiable by an associated header, thetext of the paragraph, and data elements or Formulae if applicable.Paragraphs may also include predefined icons in the margin that suggestthe content of the associated text (sometimes referred to as“emoticons”).

The pages also include names which label each page in the pagehierarchy, headers to briefly describe the page contents to a user,visited indicia to record client visits to the page (which may or maynot be displayed), a text element called “notes” which the client canuse to record his user notes pertaining to the page, and a list ofattachments which are files available for download from the page. Alsoon the page in a preferred embodiment may be indicia for “done” and“results.” The “done” indicia indicates that all information sought fromthe user has been input. The “results” indicia indicates that theresults that the system has determined are now available for a servermodule or section.

Also in a module, along with pages, are data elements. These are thebasic units for storing client information, and are catalogued accordingto the module in which they are entered. Each data unit has theattributes of name, descriptor, source, (either input or calculated),cardinality, value type, text of the question associated with the data,value, value default, the level of certainty associated with the data,expressed as a unit in a range, choice, Minimum and Maximum, numericrange, formulae for the calculated data elements, and priority for usein the maximum impact procedure and the calculated importance procedure.The nature of some of these attributes may not be apparent from theirnames. Cardinality refers to the number of values that a data elementmay have. Although most data elements may have only one value, such as anumber, others may have several values, such as context information.Value type refers to the actual data type for the data element; it maybe a Boolean operator, a number, a choice, text or of other types.Choice refers to the set of possible choices associated with a dataelement. Minimum and maximum are the defined bounds for a numeric dataelement; for example, a data element representing the number ofemployees of a company could have a minimum of zero but could not have aminimum equal to a negative number. The numeric range refers to therange bounded by the minimum and maximum values.

The data elements also include an attributed context describingmembership and constraints on usage. Regarding membership, the contextindicates the potential usage of the data elements. For example,formulae applicable for potential merger partners may be specified forthe context of an individual company that is a potential merger partnersor for a group of “merger partners.” Regarding constraint or usage, thecontext may limit the applicability of the data element. For example, adata element “revenues” with a context of 1998 would not normally beapplicable to the year 1999.

Finally, the modules may include formulae that perform operations ondata elements. The formulae can utilize logical clauses such as AND/OR,or =, and operators such as, −, *, / in the case of numeric dataelements. Functions are defined to perform special operations too; forexample, the function xyplot (title, x, y) can be used to generate agraphic element showing x as a function of y. Other special functionsare described under “Detailed Description of the a Preferred Embodiment”below.

One of the important aspects of the present invention is the ability tofocus in on a topic and a recommendation incrementally. In prior artsystems, the approach is typically to input the information necessary tomake a decision, process that information in accordance with somepredetermined algorithm, and output the result. This approach isunresponsive to the desires of the users to obtain a particular outcome,because it simply gives an answer without revealing the reasoning behindthat answer. Further, it wastes user time and processor power bycompletely processing the matter even if the user is interested in onlya preliminary conclusion or a portion of the answer.

The present system asks for the necessary information a portion at atime, processes that portion, outputs the result, and then goes toanother portion. The user is thus free to abort the process when ittakes a route that is undesirable, is incompatible with the finaldesired conclusions, or is simply sufficiently detailed for the presentpurposes without going further. This approach also enables the userbetter to see the logic at work in arriving of the conclusions, in orderto experiment with alternative input information to arrive at thedesired conclusions.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B shows the page hierarchy for a module in an embodimentof the present invention.

FIG. 2 shows a first substantive page that briefly describes the purposeof the module in an embodiment of the present invention.

FIG. 3 shows another informational page, explaining to the user themanner in which the module operates in an embodiment of the presentinvention.

FIGS. 4-7 show pages of the module that request user data input in anembodiment of the present invention.

FIGS. 8A, 8B and 8C show the preliminary recommendations presented tothe user in an embodiment of the present invention.

FIGS. 9A and 9B show the page hierarchy of the business combinationsubmodule section of the module in an embodiment of the presentinvention.

FIG. 10 shows an explanation of the point at issue in the businesscombination submodule in an embodiment of the present invention.

FIG. 11 shows a page which describes the methods required for thebusiness combination submodule section in an embodiment of the presentinvention.

FIGS. 12-14 show pages of the business combination submodule thatrequests user data input in an embodiment of the present invention.

FIG. 15 shows proforma financial statements in an embodiment of thepresent invention.

FIG. 16 shows a page revealing an alternative option in an embodiment ofthe present invention.

FIG. 17 shows proforma financial statements using the alternative optionin an embodiment of the present invention.

FIG. 18 shows a page devoted to defining several terms used in themodule in an embodiment of the present invention.

FIG. 19 shows a comparison of the two methods used in the businesscombination submodule in an embodiment of the present invention.

FIG. 20 shows an outline of appendices to this module in an embodimentof the present invention.

DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT

The invention can be illustrated with examples. It is important torecognize that the invention extends beyond the specific examples, andto the more general use of the hierarchical system and overallorganization and methodology, as expressed in the claims below. Theseexamples merely serve to illustrate a use of the invention.

One embodiment involves a module for structuring alternatives in aproposed investment. This module assesses strategic, tactical andoperational priorities to generate a preliminary list of potential dealstructures. This preliminary list is refined with detailed analysis andadditional user input to provide evaluations and recommendations of theseveral potential structures, the logic behind the recommendations, anda high level quantitative analysis.

FIGS. 1A and 1B show the page hierarchy for the module. In a sense, thepage hierarchy is a table of contents; however, it changes dynamicallyin the manner shown below as the system processes input information. Thefirst substantive page for the user is the “Background and RelevanceCheck” page, shown in FIG. 2. That page briefly describes the purpose ofthe module, identifies operators for which the module is intended, andalerts the user that there are certain other modules that the usershould have completed prior to undertaking this module (the merger andacquisition module, the target identification module, the due diligencemodule and the valuation module in this case). In this particularexample, these other modules are merely recommended; alternatively, thismodule could incorporate these other modules as submodules or otherwiserequire their completion as a precondition to proceeding in this module.The user advances to the next page in the module by clicking on the“Proceed to next page” phrase 22.

The next page, shown in FIG. 3, is another informational page,explaining to the user the manner in which the module operates. Thereare two main phases to the operation of this module. First, a series ofquestions are posed in order to generate input data. This data isprocessed in the manner described below to produce a preliminaryrecommendation of potential deal structures. That preliminaryrecommendation then is refined and the potential deal structures arenarrowed by posing a series of additional questions through othersections or submodules of the module.

This page in FIG. 3 also informs the user of the materials he may wishto have at hand for answering the questions to be posed, and indicatesthe type of individual who will most likely benefit from, and havesufficient expertise in using, the module, namely a chief financialofficer, chief strategy officer, or a comptroller in this particularexample. The user advances to the next page by clicking on the “BeginModule Assessments” phrase 23.

The next page, shown in FIG. 4, is the first page of the module torequest user data input. The heading 26 “Objectives Assessment” providesa shorthand summary of the page contents. A paragraph 28 bearing aheading 30 of “Background” explains briefly what is being accomplishedin the page. In other words, this page asks the user to specify as inputdata the objectives underlying the proposed insertion. The first step inthat process is to input the portions of the business sought to beacquired.

This information is elicited through a table 31 asking yes or noquestions, beginning with the question whether it is the “EntireBusiness” that is sought to be acquired. If the answer is yes, then theanswers to the remaining questions in the table which are directedtoward whether certain portions of the business are being acquired canbe ignored. If the answer to the question whether the entire business isproposed to be acquired is no, then the user goes on to answer theremaining questions in the table about which particular portions of thebusiness are being acquired.

The table 31 also elicits from the user information about the certaintyof his answers to the questions, on a scale from 1-5 in this example.This “certainty” information is used by the system in a variety of ways.In some determinations, the degree of certainty directly affects thedetermination, as is the case when a relatively high degree of certaintyis necessary before undertaking a particular kind of transaction orassuming a particular kind of risk. The certainty information is alsoused in expressing the likelihood that a given recommendation is thecorrect one; the certainty of the output is thus dependent upon thecertainty of the input. This simple syllogism becomes much morecomplicated when one recognizes that the weight given to the inputvariables varies with a different output recommendation. A relativelylow degree of certainty in a given data element may therefore result ina relatively low degree of certainty in the output recommendation forone output recommendation but not for another. The certainty informationis dealt with according to logical precepts. For example, input data xmay be highly certain and output data y may be highly uncertain. Ifcalculated data element z=x or y, then the certainty of Z is highlycertain. But if z=x·y, then the certainty of z would be highlyuncertain.

Similar to certainty information is importance information. A user mayconsider a particular outcome or recommendation to have a particulardegree of importance, or the system itself may be programmed to assignto a particular outcome or recommendation a particular degree ofimportance. For example, certain tax or accounting treatment for atransaction may be considered important under some circumstances.Notably, such treatment may not be considered important under othercircumstances, and the system can be programmed to distinguish betweensuch circumstances. The outcome or recommendation that is assigned aparticular degree of importance, like other outcomes or recommendations,is based directly or indirectly on input data, and is presented in a setof one or more display screens. The input data upon which arecommendation with an assigned degree of importance is based is alsoassigned degrees of importance. The degree of importance assigned tosuch input data is based on (1) the degree of importance assigned to therecommendation that it helps to determine, and (2) the criticality ofsuch item of input data in making that determination. Some items ofinput data, for example, will have relatively low degrees of importanceeven though they are related to recommendations of relatively highdegrees of importance, because they play only a small role indetermining those recommendations of high degrees of importance, andconversely. The assigned or determined degrees of importance of inputdata can be used to identify screens for display to the user inpresenting the recommendations and ways to change the recommendations.

Another table 32 on this page solicits for input the expected price ofthe proposed acquisition together with the user's level of certaintywith respect to the price that he inputs. This information is also usedin subsequent pages. The user advances to the next page by clicking onthe “Proceed to next page,” phrase 34 of this page.

The next page, shown in FIG. 5, asks for information about the user'sdesired control over the business or particular assets being acquired.As in the previous page, there is a page name 36 and a paragraph 38 witha heading 40. The information in this example is elicited through atable 42 setting forth the business portions being acquired andproviding a set of four choices for the user: “Unilateral control,”“Shared Control,” “Influence” or “No Control (passive).” The user checksa box in each row for each business portion proposed to be acquired. Theuser can then advance to the next page by clicking on the phrase 46,“Proceed to next page.”

The relationship between the page in FIG. 5 and the page in FIG. 4 isone of precondition. That is, the information sought in FIG. 4 must beentered before the information sought in FIG. 5 can be entered. Thesystem logic thus will not advance the user from the page of FIG. 4 tothe page of FIG. 5 until the page of FIG. 4 information is indeedentered. If the user seeks to advance from the page of FIG. 4 to thepage of FIG. 5 by clicking on the “Proceed to next page” phrase 34 ofthe page of FIG. 4 without that information being entered, the systemwill alert the user to the error and prompt him to correct it.

The next page is shown on FIG. 6. This page bears the heading 50“Consideration: Planned Payment,” a paragraph 52 with the heading 54“Background” and the paragraph 56 with the heading 53 “ConsiderationAssessment Tool.” It also includes a paragraph 58 that takes the form ofa table for the user to enter input data dividing the purchase priceinto categories of consideration. The price may include, for example,the payment of cash, the assumption of debt, the transfer of equity, andso on. The user proceeds to the next page by clicking on the “Proceed tonext page” phrase 59.

The next page, shown in FIG. 7, bears the name 54 “Measuring Success”and includes table 55 entitled “Success Measurement Assessment Tool.”This table elicits input data about the metrics that the user will useto measure whether the proposed acquisition shall have been successful,and the certainty of the answers. These pages complete the informationpresented to the user and solicit the necessary information for thesystem to make preliminary recommendations.

The system makes these preliminary recommendations based upon decisiontrees established with the assistance of experts in the field that isthe subject of the decision. In the example here, the decision tree isestablished with the help of accounting and business consulting experts.In practice, this is done by an iterative process to produce a “decisionfree” type algorithm. A trained individual begins by interviewing one ormore experts in the field, and reducing their input to a schematic ornarrative decision tree that covers the principal scenarios. One or moresubsequent interviews then refines and elaborates upon the decisiontree. The decision tree can be tested with actual or hypothetical inputdata with respect to which certain correct outputs are expected. Ifthere is a discrepancy between the actual outputs of the system and theexpected outputs, or a bug in obtaining outputs, the bugs ordiscrepancies can be corrected. Finally, the decision tree is translatedinto a computer language and programmed into the computer.

The preliminary recommendations are presented on the next page, shown inFIGS. 8A, 8B and 8C. It can be seen that the system has preliminarilyrecommended ten alternative arrangements for the proposed transaction,listed in a table 62 under the column 64 “Deal Structure.” The textunder the next column 65 briefly describes each alternative. Note theApplicability 66 of each alternative may be a numerical ranking of therecommendations, a presentation of special issues for consideration, orother information. The user at this point in the process now has ageneral sense of some of the factors that dictate a structure for histransaction and a description of several alternative structures for histransaction. The next step is to refine the analysis.

Before proceeding to that next step, however, the user can change hisinput data to observe the resultant changes in the preliminaryrecommendations. This is done by going to the page hierarchy or modulehome page revisiting the pages to be changed, making the desired change,and returning to the Preliminary Recommendations page shown in FIGS. 8A,8B and 8C. The change in input data can be either with respect to thesubstantive data itself or with respect to the degree of certaintyattached to particular data. This is a very valuable way for the user tounderstand the importance and weight assigned to the factual scenariosurrounding the proposed transaction.

The page hierarchy shown in FIG. 1 functions as a dynamic table ofcontents. When the user has entered sufficient input information and thesystem has processed that information sufficiently to develop someconclusions or preliminary output, the initial standard page hierarchyshown in FIG. 1 can be revised to so reflect. The revision can take theform of presenting a page hierarchy that outlines some of the topicsthat have been determined to be applicable in greater detail, ordeleting others determined not to be applicable, or substituting awholly different page hierarchy. The system thus incrementally receivesnecessary input information, processes that information, presentsresults of that processing, and sends additional information based onthe results of the processing. Unlike some prior art systems the presentsystem does not expand large amounts of processing power or consumelarge amounts of user time in inputting or processing all theinformation conceivably necessary to analyze a given topic; instead itreceives input and processes that input incrementally to arrive atoutput that is narrow and tailored to the degree desired by the user.

The business combination submodule section of the module begins with thepage hierarchy shown in FIGS. 9A and 9B, followed by the “BusinessCombination Accounting” page shown in FIG. 10. This page begins with aparagraph 66 having a short explanation of the point at issue and thentwo further paragraphs 68 and 70 outlining some of the accountingimplications to the two approaches.

Numeric examples of the two accounting methods are available to the userby clicking on the “see numeric examples” phrase 82. That links to thepage shown in FIG. 19 (see below). Additional information can beaccessed concerning FASB rulings on the accounting issues by clicking onthe “new FASB rulings” phrase 84 shown in FIG. 10. The user proceeds tothe next page by clicking on the phrase “Which method applies to mysituation” 86 shown in FIG. 10.

FIG. 11 shows the next page which describes the methods required forthis section and which allows the user to click into the page after thatby clicking on the “Proceed to next page” phrase 90.

That next page is shown in FIG. 12. Tables 92 and 94 on that page inviteresponses to two sets of questions designed to obtain facts necessary tothe determination whether the proposed transaction constitutes abusiness combination and the determination of the acquiring entity tool.

The determination of business combination states and the determinationof the acquiring entity tool is done through a decision tree preparedwith the assistance of expert professional or consulting firms. As inthe case of certain other modules, this is an interactive process. Aperson trained in the system first interviews a professional in thetopic at issue, such as a lawyer or a tax accountant in the case of atopic involving legal or tax implications. This initial interview isdesigned to produce the broad outlines of a decision algorithm or“decision tree.” It may be facilitated with the use of templatesdesigned for producing decision algorithms or used in the past insimilar decision algorithms. Once a preliminary decision algorithm isconstructed, it can be refined and detailed by further interviews withthe professional. After it is tentatively completed; it can be testedwith the use of real or hypothetical data with respect to which thecorrect decision outcome is already known in order to determine whetherthere are discrepancies in the result or bugs in the processing. Thesecan then be corrected, the algorithm retested, and the testing andcorrection procedure repeated until the system proves satisfactory. Thealgorithm is ultimately translated into computer code using a suitablecomputer language, and loaded into the system.

The algorithms used for processing and displaying data are convenientlycategorized into several groups. There is computational logic whichserves to process input data, versus presentation logic which serves todetermine the screens, pages, sections, modules and submodules to bedisplayed. Within the category of computational logic are arithmeticlogic and operations logic. Arithmetic logic is used to mathematicallyoperate on input data that is in numeric form, typically to deriveoutput data or intermediate data that also is in numeric form. Thearithmetic logic is thus mathematical in nature. A simple example ofarithmetic logic is an algorithm that computes profits by subtractingexpenses and depreciation from revenue. Operations logic is used tooperate on input data that is in non-numeric form, typically to deriveoutput data or intermediate data that also is in non-numeric form. Anexample of operations logic is an algorithm that determines whetherlong-term capital gains treatment is available under the tax laws inconnection with a transaction based upon input describing the nature ofan asset that is the subject of the transaction.

The presentation logic in the system includes navigational logic anddisplay logic. Navigational logic is used to determine which screens aredisplayed to a user based on the results of the computational logic. Forexample, if the computational logic determines that long term capitalgains treatment is available under the tax laws for a particulartransaction, then the navigational logic may present screens associatedwith that determination rather than screens associated with alternativedeterminations such as a determination that only short term capitalgains treatment is available. The display logic determines theparticular presentation of the screens identified by the navigationallogic, i.e., the formatting issues.

Continuing with reference to the figures, if the system determination isthat the proposed transaction is indeed a business combination, thenclicking on the “Proceed to next page” phrase 98 accesses the page shownin FIG. 13 to determine whether the pooling method of accounting isavailable for the transaction. It can be seen that this page checksinput data in three categories through the presentation of three tablesof questions 102, 104 and 106.

If the system determination is that pooling is available, then clickingon the “Proceed to next page” phrase 108 will access the page shown inFIG. 14. The user can then assess the impact of the proposed acquisitionor the company financial statements under a pooling accounting system.

Clicking on the “Proceed to next page” phrase 112 brings up the pageshown in FIG. 15. The table 113 in this page shows proforma financialstatements under pooled accounting. Some of the data in this page willbe the result of system calculations, while some will be simply the datainput by the user in response to specific questions in the precedingpages, such as cash, net operating loss and the like. The poolingsection is thus complete, and clicking on the “Back to main module”phrase 114 redirects the user to the page shown in FIGS. 8A, 8B and 8C.

The system may determine from the input data elicited up to and throughthe page shown in FIG. 13 that pooling is not available. In that event,upon clicking on the “Proceed to next page” phrase 108 in the page ofFIG. 13, the user is directed to the page of FIG. 16 for application ofthe Purchase Method of Accounting. Acquiring companies typically desirethe pooling method of accounting over the purchase method, and so thispage leads with a list of the reasons 115 that pooling was determined tobe unavailable in order of importance. This list allows the user toconsider modifying the structure of the transaction, to address thosereasons and inputting the data corresponding to the modified transactionin an effort to obtain pooling treatment. These reasons listed areunique to the data input and are derived from the algorithms created atthe outset from the decision tree iterations.

The next paragraph explains that this page assists the user in analyzingthe impact of the proposed transaction on the company's financialstatement using the purchase method of accounting. The following table116 requests input data in response to questions of concern to thepurchase method accounting computations, and the user is then invited toaccess the next. page.

The next page, shown in FIG. 17, shows on a table 118 the proformaresults of the proposed transaction or the financial statements of theacquiring company using the purchase method of accounting. Again, someof these numerical results are computed, while some are simply drawnfrom the input data. As in the case of the pooling method section, theuser then returns to the non-module page shown in FIGS. 8A, 8B and 8C bychecking on the “Back to main module” phrase 120.

FIG. 18 shows a page devoted to defining several terms used in themodule for the convenience of the user. This page is accessed from thepage shown in FIG. 1, the Module Home page. FIG. 19 shows a page devotedto an example of the pooling method versus the purchase method ofaccounting in a hypothetical acquisition, so that the user can see howthe two methods compare correctly, which also is accessed from theModule Home page of FIG. 1. FIG. 20 shows an outline of appendices tothis module describing certain additional ramifications of the outcome.This page is accessed through the page hierarchy of FIG. 9.

It can be appreciated from the above examples that the system is basedupon the combination of several advantageous properties. It presentspure information to assist in educating the user about the matter underconsideration. Moreover, this information is accessible at the points inthe process where it is applicable. Thus, the system does not attempt tocreate an expert out of a non-expert all at once and at the outset.Rather, the system provides useful and practical information at the timethe user needs to use it.

Further, the system is organized in such a way that the user can useonly so much of it as desired. If the user is initially interested in apreliminary recommendation or set of recommendations, rather than afarther refinement, that alone is available. If a user is interested inquickly seeing the effect or the recommendations of changing the inputdata, that is available. If a user wishes to use one module but notanother, that is generally available.

The hierarchy of modules, submodules, sections, subsections and pages,and their interrelationship with the data elements and formulae isimportant. By making the completion of certain pages, or the entering ofcertain data, or the making of certain determinations by the system,preconditions to entering subsequent pages, the system ensures that theuse of the system is tailored to the particular circumstances of theuser.

1-47. (canceled)
 48. A method of presenting content to a user with theaid of a computer and a display screen in association with the computer,comprising: a. displaying a screen set containing content; b. optionallysoliciting input data from the user on some or all display screens, andinputting said set of input data; c. optionally making input and contextsensitive content available to assist the user in inputting the inputdata; d. processing the input data through at least a portion of analgorithm to determine further content to display, input data tosolicit, or modification of previous input data;
 49. A method forassisting a user in a process of decision-making or analysis involving atopic, with the aid of a computer and a display screen in associationwith the computer and an application on the computer, comprising usingthe display, computer, and application for; a. displaying a screen setsoliciting a set of input data, and inputting said set of input data,wherein at least some of the data is characterized with a valuerepresenting a degree of certainty regarding the data; b. optionallyprocessing the input data through at least a portion of an algorithm todetermine further content to display, input data to solicit, ormodification of previous input data; c. displaying a screen or screenset showing the current recommendation or analysis where the degree ofcertainty regarding the input data is reflected in the recommendation,calculations, and/or output. d. optionally providing information orlinks in step (c) to modify those inputs which are determined to havethe highest importance and thus impact on the current recommendations.e. optionally providing information or links in step (c) to modify thoseinputs which are determined to have the greatest effect on thecalculated certainty of the current recommendation or analysis. f.optionally providing information or links in step (c) to modify thoseinputs which are determined to have the greatest effect on thecalculated certainty of a particular calculated result.
 50. A method forassisting a user in a process of decision-making or analysis involving atopic, with the aid of a computer and a display screen in associationwith the computer and an application on the computer, comprising usingthe display, computer, and application for: a. displaying a screen sethaving information concerning the topic; b. displaying a screen setsoliciting a set of input data, and inputting said set of input data,wherein optionally some of the data is characterized with a valuerepresenting a degree of certainty regarding the data; c. determiningand displaying a current recommendation generated iteratively byprocessing the input data through at least a portion of the algorithm;d. providing the option to accept the current recommendation as a finalrecommendation and ending the processing; e. displaying a screen setsoliciting additional input data and for modifying previous input dataas desired when the current recommendation is not accepted as a finalrecommendation, the contents of said screen set being dependent on anddetermined by step (c), and inputting said additional input data and/ormodified previous input data; f. repeating steps (c) and (d), and (e) asdesired; and g. displaying a screen set showing the currentrecommendation or analysis. h. optionally providing information or linksin step (c) to modify those inputs which are determined to have thehighest importance and thus impact on the current recommendations. i.optionally providing information or links in step (c) to modifythose-inputs which are determined to have the greatest effect on thecalculated certainty of the current recommendation or analysis. j.optionally providing information or links in step (c) to modify thoseinputs which are determined to have the greatest effect on thecalculated certainty of a particular calculated result.